Interface administrators, Administrators (Semantic MediaWiki), Curators (Semantic MediaWiki), Editors (Semantic MediaWiki), Suppressors, Administrators
7,785
edits
Training loss: Difference between revisions
no edit summary
No edit summary |
No edit summary |
||
| Line 8: | Line 8: | ||
Machine learning employs a variety of [[loss function]]s, depending on the problem being solved and the model being employed. Some commonly employed loss functions include: | Machine learning employs a variety of [[loss function]]s, depending on the problem being solved and the model being employed. Some commonly employed loss functions include: | ||
#Mean | #[[Mean squared error]] (MSE) Loss: This loss function is the most commonly employed for [[regression]] problems. It measures the average squared difference between predicted output and actual output. | ||
#Binary | #[[Binary cross-entropy]] (BCE): Used in [[binary classification]] problems where the objective is to accurately predict one of two possible [[class]]es, this statistic measures the difference between the predicted probability of a [[positive class]] and actual [[binary label]]. | ||
#Categorical | #[[Categorical cross-entropy]] (CCE): Used in multiclass classification problems to predict one of several classes, this statistic measures the difference between a predicted probability distribution and an actual one-hot encoded class label. | ||
#Softmax Cross-Entropy Loss: This approach is used for multiclass classification problems with mutually exclusive classes. It calculates the categorical cross-entropy loss for each class and then takes its average across all classes. | #Softmax Cross-Entropy Loss: This approach is used for multiclass classification problems with mutually exclusive classes. It calculates the categorical cross-entropy loss for each class and then takes its average across all classes. | ||